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High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates

This manuscript describes a new method for forming basal-in MCF10A organoids using commercial 384-well ultra-low attachment (ULA) microplates and the development of associated live-cell imaging and automated analysis protocols. The use of a commercial 384-well ULA platform makes this method more bro...

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Autores principales: Lee, Soojung, Chang, Jonathan, Kang, Sung-Min, Parigoris, Eric, Lee, Ji-Hoon, Huh, Yun Suk, Takayama, Shuichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8748891/
https://www.ncbi.nlm.nih.gov/pubmed/35013350
http://dx.doi.org/10.1038/s41598-021-03739-1
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author Lee, Soojung
Chang, Jonathan
Kang, Sung-Min
Parigoris, Eric
Lee, Ji-Hoon
Huh, Yun Suk
Takayama, Shuichi
author_facet Lee, Soojung
Chang, Jonathan
Kang, Sung-Min
Parigoris, Eric
Lee, Ji-Hoon
Huh, Yun Suk
Takayama, Shuichi
author_sort Lee, Soojung
collection PubMed
description This manuscript describes a new method for forming basal-in MCF10A organoids using commercial 384-well ultra-low attachment (ULA) microplates and the development of associated live-cell imaging and automated analysis protocols. The use of a commercial 384-well ULA platform makes this method more broadly accessible than previously reported hanging drop systems and enables in-incubator automated imaging. Therefore, time points can be captured on a more frequent basis to improve tracking of early organoid formation and growth. However, one major challenge of live-cell imaging in multi-well plates is the rapid accumulation of large numbers of images. In this paper, an automated MATLAB script to handle the increased image load is developed. This analysis protocol utilizes morphological image processing to identify cellular structures within each image and quantify their circularity and size. Using this script, time-lapse images of aggregating and non-aggregating culture conditions are analyzed to profile early changes in size and circularity. Moreover, this high-throughput platform is applied to widely screen concentration combinations of Matrigel and epidermal growth factor (EGF) or heparin-binding EGF-like growth factor (HB-EGF) for their impact on organoid formation. These results can serve as a practical resource, guiding future research with basal-in MCF10A organoids.
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spelling pubmed-87488912022-01-11 High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates Lee, Soojung Chang, Jonathan Kang, Sung-Min Parigoris, Eric Lee, Ji-Hoon Huh, Yun Suk Takayama, Shuichi Sci Rep Article This manuscript describes a new method for forming basal-in MCF10A organoids using commercial 384-well ultra-low attachment (ULA) microplates and the development of associated live-cell imaging and automated analysis protocols. The use of a commercial 384-well ULA platform makes this method more broadly accessible than previously reported hanging drop systems and enables in-incubator automated imaging. Therefore, time points can be captured on a more frequent basis to improve tracking of early organoid formation and growth. However, one major challenge of live-cell imaging in multi-well plates is the rapid accumulation of large numbers of images. In this paper, an automated MATLAB script to handle the increased image load is developed. This analysis protocol utilizes morphological image processing to identify cellular structures within each image and quantify their circularity and size. Using this script, time-lapse images of aggregating and non-aggregating culture conditions are analyzed to profile early changes in size and circularity. Moreover, this high-throughput platform is applied to widely screen concentration combinations of Matrigel and epidermal growth factor (EGF) or heparin-binding EGF-like growth factor (HB-EGF) for their impact on organoid formation. These results can serve as a practical resource, guiding future research with basal-in MCF10A organoids. Nature Publishing Group UK 2022-01-10 /pmc/articles/PMC8748891/ /pubmed/35013350 http://dx.doi.org/10.1038/s41598-021-03739-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lee, Soojung
Chang, Jonathan
Kang, Sung-Min
Parigoris, Eric
Lee, Ji-Hoon
Huh, Yun Suk
Takayama, Shuichi
High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates
title High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates
title_full High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates
title_fullStr High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates
title_full_unstemmed High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates
title_short High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates
title_sort high-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8748891/
https://www.ncbi.nlm.nih.gov/pubmed/35013350
http://dx.doi.org/10.1038/s41598-021-03739-1
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